Summary of Selecting Subsets Of Source Data For Transfer Learning with Applications in Metal Additive Manufacturing, by Yifan Tang et al.
Selecting Subsets of Source Data for Transfer Learning with Applications in Metal Additive Manufacturing
by Yifan Tang, M. Rahmani Dehaghani, Pouyan Sajadi, G. Gary Wang
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a systematic approach to transfer learning (TL) in metal additive manufacturing (AM). The authors aim to improve modeling performance in target domains by selecting relevant subsets of source data based on similarities between source and target datasets. They develop a Pareto frontier-based source data selection method, which is integrated into both instance-based and model-based TL methods. Experimental results demonstrate that the proposed approach can find a small subset of source data with better TL performance compared to using all source data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn from each other’s experiences in metal printing. When we don’t have enough information, we can use what we already know to make better predictions. The authors created a way to choose the most useful pieces of information from past printings to help with new ones. This method works well and is easy to apply to different situations. |
Keywords
* Artificial intelligence * Transfer learning
